Part 1 was the shape of the thing. This part is what it does. A model only earns its keep under real work : dropping the fleet on a codebase it has never seen, pushing one change across repositories, and keeping a decision trail that survives a restart. This…
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This is Part 1 of two : the problem and the StyloAgent model. Part 2 : The Workflow in Practice applies it. Code generation stopped being the problem a while ago. The models write good code. They follow the conventions in the file they are editing, and if you…
StyloBot v8 lets a site give AI crawlers a controlled Markdown view of its pages at its own local edge, without ceding the gateway to a CDN. This piece is about the extractor that does the conversion (StyloExtract): the walker bug lucidVIEW caught, and the…
Bot detection should not stop at allow/block. This post shows how StyloBot's classification result becomes application logic in ASP.NET: tag helpers in Razor, action filters and policies in controllers, signal-level gating, so your UI can shape the experience…
Bot detection should not stop at allow/block. This post shows how StyloBot's classification result becomes application logic in TypeScript: server middleware for Express and Fastify, template helpers for Handlebars, Nunjucks, and EJS, and browser web…
Identity-based bot detection (IPs, user-agents, headers) collapses the moment automation starts rotating identities. StyloBot models clients as behavioural shapes in a 130+ dimensional vector space. Here's why that's the right level of abstraction for level-4…
How do you test a detector whose answer is supposed to change as it learns? This post is about BDF, the Behavioural Definition Format that makes StyloBot testable: one file defines a class of traffic, then drives regression replay, load generation, and a…
StyloBot's detection engine is ASP.NET Core. This post explains how that engine connects to Go gateways, Node.js applications, and any other stack via a gRPC sidecar, a typed Go SDK, and a Caddy plugin, without any of those consumers needing to know anything…
Long-running services tend to slowly drift toward unbounded memory. Here's how I find that drift, how I think about fixing it without papering over it, and a worked example from StyloBot's vector similarity layer that took it from 13 GB on the Large Object…
Heads up: this one is for the nerds. It's a deep technical dive into StyloBot's adaptive learning system (EWMA updates, hysteresis thresholds, verdict caching, variance watchdogs), with real code from Mostlylucid.BotDetection and pointers to the equivalent…
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